Customer data is a form of fuel for innovation and competitiveness. Servicing everything from artificial intelligence algorithms to business decision-making processes. However, customer data management often presents itself as a double-edged sword, capable of both enhancing and impeding progress. The challenges are multifaceted encompassing questions around ethics, governance, privacy, security, and architectural complexity.
Effective data governance is imperative for ensuring data is managed responsibly and in alignment with organizational objectives. Some specific governance challenges affect multiple sectors and the tech sector has its fair share of these.
Determining clear ownership and responsibility for customer data is a fundamental but often complex aspect of customer data management within the tech sector; it involves specifying who within the organization is accountable for the creation, maintenance, and security of the data. While this may sound straightforward in theory, the practical implementation can be riddled with challenges, often resulting in internal conflicts and the inadvertent creation of data silos.
Many companies, especially large ones, have intricate organizational structures with multiple departments and teams. Each business unit may generate and utilize data for various purposes. Determining who owns the data or even attributes of them in this complex web of organizational units can be daunting. Different departments may vie for ownership, leading to conflicts and disagreements.
The data should flow seamlessly across different functions and teams. Customer data collected by the marketing team may be crucial for the sales teams as well. Deciding who has the ultimate ownership of such shared data may lead to internal disputes. Without a clear resolution, different departments may duplicate efforts or make decisions based on incomplete or inconsistent information.
Without a well-established data governance framework that outlines data ownership and responsibilities, confusion can prevail. When employees are uncertain about who is responsible for the data, they may assume ownership, leading to data redundancy, inefficiencies, and conflicting data definitions.
In the absence of this clear and categorical ownership, some departments or individuals may adopt a “data hoarder” mentality. They might be reluctant to share data with others or make it inaccessible, due to a fear of loss of control. This behavior not only hinders collaboration but also results in data silos, where valuable information is isolated within specific teams or units, making it inaccessible to others who may need it.
The various consumer privacy and consumer data control regulations mandate that organizations have clear data ownership and accountability structures in place. Failure to do so can result in legal penalties and reputational damage.
Maintaining data accuracy and consistency across diverse customer data sources also poses an ongoing challenge especially when accompanied by managing data from creation through to disposal all the while preserving its value. This is a complex and dynamic endeavor especially if it is done without specialized CMDM tooling.
Companies are also dynamic entities that evolve, mergers, acquisitions, restructures, and shifts in leadership complicate the issue of data ownership, and what was clear one day may become muddled the next as roles and responsibilities change.
The Pretectum CMDM offers a range of features and capabilities that can help address these challenges.
Effective data governance in Pretectum CMDM defines data ownership, roles, and responsibilities within the organization, as well as mechanisms for enforcing data governance policies.
The built-in privacy controls of Pretectum CMDM and security measures to protect sensitive data include encryption, access controls, and customizable compliance with data protection regulations.
From data ingestion through data lifecycle management the Pretectum CMDM is tooled for data quality monitoring, data cleansing, and data integration to ensure that data is accurate, consistent, and accessible across your organization.
The inherent nature of Pretectum CMDM facilitates collaboration among different teams and departments, allowing them to share and access data securely and transparently.
Built on the latest scalable cloud architecture, Pretectum CMDM can handle growing data volumes and support modern data management practices, including integration and big data analytics.
To understand how Pretectum CMDM specifically addresses these challenges we recommend reaching out to us or referring to our documentation and user guides. Additionally, you may want to seek out user reviews and case studies to learn about real-world experiences with the platform and how it can help your organization overcome your customer data management challenges.